Amazon ML Challenge 2026 Team & ML Stack Architect

1. Team Composition & Eligibility

3 Members

Team Name

Roster (Strictly 3–4 Members) Cross-College Allowed

Eligibility Rules Check

Team Size: 3 or 4 members strictly

Programs: B.E./B.Tech, M.E./M.Tech, M.S. or PhD only

Graduation Year: Must be 2027 or 2028

AWS Builder Center: Valid Profile ID required per student

One student can participate in only one team

2. 72-Hour ML Pipeline & Benchmark

Target: F1-Macro / NDCG

Select ML Model Components

LightGBM Baseline

Fast gradient boosting on engineered tabular & text TF-IDF tokens. (Latency: 6ms)

PyTorch Feature Extractor

Fine-tuned MiniLM / DeBERTa sequence embeddings for semantic parsing. (Latency: 28ms)

Ensemble Stacker

Multi-fold out-of-fold blending with Logistic Regression meta-classifier. (Latency: 8ms)

ResNet Vision Backing

Multimodal image embedding pass for catalog product assets. (Latency: 45ms)

Verification & Simulation Telemetry

Eligibility Status Eligible (3 Valid Members, Cross-College Approved)

Simulated Benchmark Score 0.8842 NDCG

Estimated P95 Latency 42 ms

Challenge Readiness Sprint Ready

Audit & Registration Payload Log

Ready. Default representative team loaded successfully.

Amazon ML Challenge Stack Architect & Telemetry Simulation

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The Amazon ML Challenge Architect validates squad compliance and simulates pipeline benchmark metrics across a seventy-two hour sprint. Baseline performance starts at zero point seven zero NDCG. Each selected component adds calibrated predictive lift alongside latency overhead. Toggling on ResNet Vision Backing adds zero point zero two one five NDCG while increasing inference latency by forty-five milliseconds. Clicking Export Team Dossier generates the complete submission JSON payload with AWS profile identifiers, pipeline modules, and simulated telemetry.